{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/Qiskit/qiskit-tutorials/master/images/qiskit-heading.png\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" width=\"500 px\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## _*The Bernstein-Vazirani Algorithm*_ \n",
    "\n",
    "In this tutorial, we introduce the [Bernstein-Vazirani algorithm](http://epubs.siam.org/doi/abs/10.1137/S0097539796300921), which is one of the earliest algorithms demonstrating the power of quantum computing. Despite its simplicity, it is often used and is the inspiration for many other quantum algorithms even today; it is the basis of the power of the short-depth quantum circuits, as in [Bravyi et al.](https://arxiv.org/abs/1704.00690) that uses its non-oracular version, or in [Linke et al.](http://www.pnas.org/content/114/13/3305.full) that uses it to test the performance of the quantum processors (see also the [talk by Ken Brown](https://www.youtube.com/watch?v=eHV9LTiePrQ) at the ThinkQ 2017 conference). Here, we show the implementation of the Bernstein-Vazirani algorithm **without using entanglement** based on [Du et al.](https://arxiv.org/abs/quant-ph/0012114) on Qiskit and test it on IBM Q systems. \n",
    "\n",
    "The latest version of this notebook is available on https://github.com/qiskit/qiskit-tutorial.\n",
    "\n",
    "***\n",
    "### Contributors\n",
    "Rudy Raymond\n",
    "\n",
    "### Qiskit Package Versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'qiskit': '0.10.3',\n",
       " 'qiskit-terra': '0.8.1',\n",
       " 'qiskit-ignis': '0.1.1',\n",
       " 'qiskit-aer': '0.2.1',\n",
       " 'qiskit-ibmq-provider': '0.2.2',\n",
       " 'qiskit-aqua': '0.5.1'}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import qiskit\n",
    "qiskit.__qiskit_version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction <a id='introduction'></a>\n",
    "\n",
    "The Bernstein-Vazirani algorithm deals with finding a hidden integer $a \\in \\{0,1\\}^n$ from an oracle $f_a$ that returns a bit $a \\cdot x \\equiv \\sum_i a_i x_i \\mod 2$ upon receiving an input $x \\in \\{0,1\\}^n$. A classical oracle returns $f_a(x) = a \\cdot x \\mod 2$ given an input $x$. Meanwhile, a quantum oracle behaves similarly but can be queried with superposition of input $x$'s. \n",
    "\n",
    "Classically, the hidden integer $a$ can be revealed by querying the oracle with $x = 1, 2, \\ldots, 2^i, \\ldots, 2^{n-1}$, where each query reveals the $i$-th bit of $a$ (or, $a_i$). For example, with $x=1$ one can obtain the least significant bit of $a$, and so on. This turns out to be an optimal strategy; any classical algorithm that finds the hidden integer with high probability must query the oracle $\\Omega(n)$ times. However, given a corresponding quantum oracle, the hidden integer can be found with only $1$ query using the Bernstein-Vazirani algorithm. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Algorithm\n",
    "\n",
    "The Bernstein-Vazirani algorithm to find the hidden integer is very simple: start from a $|0\\rangle$ state, apply Hadamard gates, query the oracle, apply Hadamard gates, and measure. The correctness of the algorithm is best explained by looking at the transformation of a quantum register $|a \\rangle$ by $n$ Hadamard gates, each applied to the qubit of the register. It can be shown that\n",
    "\n",
    "$$\n",
    "|a\\rangle \\xrightarrow{H^{\\otimes n}} \\frac{1}{\\sqrt{2^n}} \\sum_{x\\in \\{0,1\\}^n} (-1)^{a\\cdot x}|x\\rangle.\n",
    "$$\n",
    "\n",
    "In particular, when we start with a quantum register $|0\\rangle$ and apply $n$ Hadamard gates to it, we have the familiar quantum superposition as below\n",
    "\n",
    "$$\n",
    "|0\\rangle \\xrightarrow{H^{\\otimes n}} \\frac{1}{\\sqrt{2^n}} \\sum_{x\\in \\{0,1\\}^n} |x\\rangle,\n",
    "$$\n",
    "\n",
    "which is slightly different from the Hadamard transform of the reqister $|a \\rangle$ by the phase $(-1)^{a\\cdot x}$. \n",
    "\n",
    "Now, the quantum oracle $f_a$ returns $1$ on input $x$ such that $a \\cdot x \\equiv 1 \\mod 2$, and returns $0$ otherwise. This means we have the following transformation:\n",
    "\n",
    "$$\n",
    "|x \\rangle \\left(|0\\rangle - |1\\rangle \\right) \\xrightarrow{f_a} | x \\rangle \\left(|0 \\oplus f_a(x) \\rangle - |1 \\oplus f_a(x) \\rangle \\right) = (-1)^{a\\cdot x} |x \\rangle \\left(|0\\rangle - |1\\rangle \\right). \n",
    "$$\n",
    "\n",
    "Notice that the second register $|0\\rangle - |1\\rangle$ in the above does not change and can be omitted for simplicity. In short, the oracle can be used to create $(-1)^{a\\cdot x}|x\\rangle$ from the input $|x \\rangle$. In this tutorial, we follow [Du et al.](https://arxiv.org/abs/quant-ph/0012114) to generate a circuit for a quantum oracle without the need of an ancilla qubit (often used in the standard quantum oracle). \n",
    "\n",
    "The algorithm to reveal the hidden integer follows naturally by querying the quantum oracle $f_a$ with the quantum superposition obtained from the Hadamard transformation of $|0\\rangle$. Namely,\n",
    "\n",
    "$$\n",
    "|0\\rangle \\xrightarrow{H^{\\otimes n}} \\frac{1}{\\sqrt{2^n}} \\sum_{x\\in \\{0,1\\}^n} |x\\rangle \\xrightarrow{f_a} \\frac{1}{\\sqrt{2^n}} \\sum_{x\\in \\{0,1\\}^n} (-1)^{a\\cdot x}|x\\rangle.\n",
    "$$\n",
    "\n",
    "Because the inverse of the $n$ Hadamard gates is again the $n$ Hadamard gates, we can obtain $a$ by\n",
    "\n",
    "$$\n",
    "\\frac{1}{\\sqrt{2^n}} \\sum_{x\\in \\{0,1\\}^n} (-1)^{a\\cdot x}|x\\rangle \\xrightarrow{H^{\\otimes n}} |a\\rangle.\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The (Inner-Product) Oracle <a id='oracle'></a>\n",
    "\n",
    "Here, we describe how to build the oracle used in the Bernstein-Vazirani algorithm. The oracle is also referred to as the [inner-product oracle](https://arxiv.org/pdf/quant-ph/0108095.pdf) (while the oracle of the Grover search is known as the Equivalence, or EQ, oracle). Notice that it transforms $|x\\rangle$ into $(-1)^{a\\cdot x} |x\\rangle$. Clearly, we can observe that\n",
    "\n",
    "$$\n",
    "(-1)^{a\\cdot x} = (-1)^{a_1 x_1} \\ldots (-1)^{a_ix_i} \\ldots (-1)^{a_nx_n} = \\prod_{i: a_i = 1} (-1)^{x_i}. \n",
    "$$\n",
    "\n",
    "Therefore, the inner-product oracle can be realized by the following unitary transformation, which is decomposable as single-qubit unitaries: \n",
    "\n",
    "$$\n",
    "O_{f_a} = O^1 \\otimes O^2 \\otimes \\ldots \\otimes O^i \\otimes \\ldots \\otimes O^n, \n",
    "$$\n",
    "where $O^i = (1 - a_i)I + a_i Z$, where $Z$ is the Pauli $Z$ matrix and $I$ is the identity matrix for $a_i \\in \\{0,1\\}$. \n",
    "\n",
    "Notice that we start from a separable quantum state $|0\\rangle$ and apply a series of transformations that are separable (i.e., can be described by unitaries acting on a single qubit): Hadamard gates to each qubit, followed by the call to the *decomposable* quantum oracle as [Du et al.](https://arxiv.org/abs/quant-ph/0012114), and another Hadamard gate. Hence, there is no entanglement created during the computation. This is in contrast with the circuit at [Linke et al.](http://www.pnas.org/content/114/13/3305.full) that used CNOT gates to realize the oracle and an ancilla qubit to store the answer of the oracle. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Circuit <a id=\"circuit\"></a>\n",
    "\n",
    "We now implement the Bernstein-Vazirani algorithm with Qiskit by first preparing the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-26T15:37:43.600800Z",
     "start_time": "2018-09-26T15:37:42.133475Z"
    }
   },
   "outputs": [],
   "source": [
    "#initialization\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "\n",
    "# importing Qiskit\n",
    "from qiskit import IBMQ, BasicAer\n",
    "from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, execute\n",
    "from qiskit.compiler import transpile\n",
    "from qiskit.tools.monitor import job_monitor\n",
    "\n",
    "# import basic plot tools\n",
    "from qiskit.tools.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-26T15:37:44.514394Z",
     "start_time": "2018-09-26T15:37:43.603203Z"
    }
   },
   "outputs": [],
   "source": [
    "# Load our saved IBMQ accounts.\n",
    "IBMQ.load_accounts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first set the number of qubits used in the experiment, and the hidden integer $a$ to be found by the Bernstein-Vazirani algorithm. The hidden integer $a$ determines the circuit for the quantum oracle. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-26T15:37:45.913084Z",
     "start_time": "2018-09-26T15:37:45.909662Z"
    }
   },
   "outputs": [],
   "source": [
    "nQubits = 14 # number of physical qubits\n",
    "a = 101      # the hidden integer whose bitstring is 1100101\n",
    "\n",
    "# make sure that a can be represented with nQubits\n",
    "a = a % 2**(nQubits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then use Qiskit to program the Bernstein-Vazirani algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-26T15:37:47.277053Z",
     "start_time": "2018-09-26T15:37:47.265832Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x1216131d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating registers\n",
    "# qubits for querying the oracle and finding the hidden integer\n",
    "qr = QuantumRegister(nQubits)\n",
    "# for recording the measurement on qr\n",
    "cr = ClassicalRegister(nQubits)\n",
    "\n",
    "circuitName = \"BernsteinVazirani\"\n",
    "bvCircuit = QuantumCircuit(qr, cr)\n",
    "\n",
    "# Apply Hadamard gates before querying the oracle\n",
    "for i in range(nQubits):\n",
    "    bvCircuit.h(qr[i])\n",
    "    \n",
    "# Apply barrier so that it is not optimized by the compiler\n",
    "bvCircuit.barrier()\n",
    "\n",
    "# Apply the inner-product oracle\n",
    "for i in range(nQubits):\n",
    "    if (a & (1 << i)):\n",
    "        bvCircuit.z(qr[i])\n",
    "    else:\n",
    "        bvCircuit.iden(qr[i])\n",
    "        \n",
    "# Apply barrier \n",
    "bvCircuit.barrier()\n",
    "\n",
    "#Apply Hadamard gates after querying the oracle\n",
    "for i in range(nQubits):\n",
    "    bvCircuit.h(qr[i])\n",
    "    \n",
    "# Measurement\n",
    "bvCircuit.barrier(qr)\n",
    "bvCircuit.measure(qr, cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-26T15:37:53.798709Z",
     "start_time": "2018-09-26T15:37:49.368851Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1384.6x3449.46 with 1 Axes>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bvCircuit.draw(output='mpl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment with Simulators\n",
    "\n",
    "We can run the above circuit on the simulator. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-26T15:38:02.785014Z",
     "start_time": "2018-09-26T15:38:02.562010Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use local simulator\n",
    "backend = BasicAer.get_backend('qasm_simulator')\n",
    "shots = 1000\n",
    "results = execute(bvCircuit, backend=backend, shots=shots).result()\n",
    "answer = results.get_counts()\n",
    "plot_histogram(answer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the result of the measurement is the binary representation of the hidden integer $a$. \n",
    "\n",
    "## Experiment with Real Devices\n",
    "\n",
    "We can run the circuit on the real device as below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-26T15:38:07.311126Z",
     "start_time": "2018-09-26T15:38:07.114440Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job Status: job has successfully run\n"
     ]
    }
   ],
   "source": [
    "backend = IBMQ.get_backend('ibmq_16_melbourne')\n",
    "shots = 1000\n",
    "\n",
    "bvCompiled = transpile(bvCircuit, backend=backend, optimization_level=1)\n",
    "job_exp = execute(bvCircuit, backend=backend, shots=shots)    \n",
    "job_monitor(job_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-09-26T16:09:52.415375Z",
     "start_time": "2018-09-26T16:09:51.924230Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = job_exp.result()\n",
    "answer = results.get_counts(bvCircuit)\n",
    "\n",
    "threshold = int(0.01 * shots) #the threshold of plotting significant measurements\n",
    "filteredAnswer = {k: v for k,v in answer.items() if v >= threshold} #filter the answer for better view of plots\n",
    "\n",
    "removedCounts = np.sum([ v for k,v in answer.items() if v < threshold ]) #number of counts removed \n",
    "filteredAnswer['other_bitstrings'] = removedCounts  #the removed counts is assigned to a new index\n",
    "\n",
    "plot_histogram(filteredAnswer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'00000001000101': 22, '00100001101101': 24, '00000011110101': 12, '00100001110101': 22, '00000011100001': 10, '00100001100001': 13, '00000001100001': 21, '00000011100101': 44, '00000001100101': 216, '00100011100101': 22, '00000001110101': 38, '00000000100101': 15, '00000001100111': 10, '00100001100101': 82, '00000001100100': 13, '01100001100101': 18, '00000001101101': 46, '00000011101101': 13, '00000001110001': 12, '01000001100101': 26, 'other_bitstrings': 321}\n"
     ]
    }
   ],
   "source": [
    "print(filteredAnswer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We indeed see that the outcome is the binary representation of the hidden integer $a$ with high probability. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
